Logistic Regression (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data set\n",
    "claimants = pd.read_csv(\"claimants.csv\")\n",
    "claimants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the case number columns as it is not required\n",
    "claimants.drop([\"CASENUM\"],inplace=True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shape of the data set\n",
    "claimants.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing NA values in data set\n",
    "claimants = claimants.dropna()\n",
    "claimants.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing our data into input and output variables \n",
    "X = claimants.iloc[:,1:]\n",
    "Y = claimants.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic regression and fit the model\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict for X dataset\n",
    "y_pred = classifier.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_df= pd.DataFrame({'actual': Y,\n",
    "                         'predicted_prob': classifier.predict(X)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix for the model accuracy\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(Y,y_pred)\n",
    "print (confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "((381+395)/(381+197+123+395))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.predict_proba (X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(Y, classifier.predict_proba (X)[:,1])\n",
    "\n",
    "auc = roc_auc_score(Y, y_pred)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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